from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
from tensorflow.python.ops import resource_variable_ops


def mnist_model(x, keep_prob, use_resource=False, dtype=tf.float32):
    """
    conv 5X5X32 + relu
    max pooling 2X2
    conv 5X5X32 + relu
    max pooling 2X2
    fully connected 256 units + relu (with dropuot)
    fully connected 10 units + softmax (with dropuot)
    :param x:
    :param keep_prob:
    :return:
    """

    def weight_variable(shape, name):
        with tf.device('/cpu:0'):
            initial = tf.truncated_normal(shape, stddev=0.1, dtype=dtype)
        if use_resource:
            return resource_variable_ops.ResourceVariable(initial, name=name)
        else:
            return tf.Variable(initial, name=name)

    def bias_variable(shape, name):
        initial = tf.constant(0.1, shape=shape, dtype=dtype)
        if use_resource:
            return resource_variable_ops.ResourceVariable(initial, name=name)
        return tf.Variable(initial, name=name)

    def conv2d(x, W):
        return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

    def max_pool_2x2(x):
        return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                              strides=[1, 2, 2, 1], padding='SAME')

    with tf.variable_scope('', use_resource=use_resource):
        with tf.name_scope("Reshaping_data") as scope:
            x_image = tf.reshape(x, [-1, 28, 28, 1])
        with tf.name_scope("Conv1") as scope:
            W_conv1 = weight_variable([5, 5, 1, 32], 'Conv_Layer_1')
            b_conv1 = bias_variable([32], 'bias_for_Conv_Layer_1')
            h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
            h_pool1 = max_pool_2x2(h_conv1)
        with tf.name_scope("Conv2") as scope:
            W_conv2 = weight_variable([5, 5, 32, 64], 'Conv_Layer_2')
            b_conv2 = bias_variable([64], 'bias_for_Conv_Layer_2')
            h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
            h_pool2 = max_pool_2x2(h_conv2)

        with tf.name_scope("Fully_Connected1") as scope:
            W_fc1 = weight_variable([7 * 7 * 64, 1024], 'Fully_Connected_layer_1')
            b_fc1 = bias_variable([1024], 'bias_for_Fully_Connected_Layer_1')
            h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
            h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

        with tf.name_scope("Fully_Connected2") as scope:
            # keep_prob = tf.placeholder("float")
            h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
            if use_resource:
                W_fc2 = resource_variable_ops.ResourceVariable(tf.truncated_normal([1024, 10], stddev=0.1),
                                                               name='W_fc2')
                b_fc2 = resource_variable_ops.ResourceVariable(tf.constant(0.1, shape=[10]), name='b_fc2')
            else:
                with tf.device('/cpu:0'):
                    W_fc2 = tf.Variable(tf.truncated_normal([1024, 10], stddev=0.1), name='W_fc2')
                b_fc2 = tf.Variable(tf.constant(0.1, shape=[10]), name='b_fc2')
            net = tf.matmul(h_fc1_drop, W_fc2) + b_fc2

    return net
